To challenge ourselves, we used a seven dimensional glycolysis model to generate our testing and training data [1]}. This data was all generated from a single initial condition \(x_0=[1,0.19,0.2,0.1,0.3,0.14,0.05]\) . The augSILL model reaches a comparable 5-step prediction error to deepDMD in under 1000 training epochs and neither significantly changes over the next 4000 epochs, see Fig. REF . Note that the model we learn using the augSILL basis has 995 parameters. All in all, the deepDMD model has a total of \(3,949\)  parameters. All of the augSILL parameters are easily interpreted as center, steepness and weight parameters of a logistic or RBF.
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